Identification and functional analysis of energy metabolism and pyroptosis-related genes in diabetic nephropathy - Shan He; Jian Ye; Yu Wang; Lu yang Xie; Si Yi Liu; Qin kai Chen (Heliyon)
Background: Energy metabolism and pyroptosis are integral to the pathogenesis of diabetic nephropathy (DN). However, the precise roles of energy metabolism and pyroptosis in DN development remain unclear. This study aims to elucidate the roles of energy metabolism- and pyroptosis-related differentially expressed genes (EMAPRDEGs) in DN development. Methods: EMAPRDEGs were identified by querying the GeneCards and Gene Expression Omnibus (GEO) databases. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analysis. Additionally, mRNA-miRNA, mRNA-drug, and mRNA-transcription factor (TF) interaction networks were constructed. Differential expression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic potential of EMAPRDEGs. Immune cell infiltration in DN was assessed using the ssGSEA algorithm, and the expression levels of EMAPRDEGs in DN tissues were validated by quantitative real-time PCR (qRT-PCR). Results: Thirteen EMAPRDEGs were identified, with GO and KEGG analyses indicating their involvement in energy metabolism pathways. GSEA revealed significant enrichment of these genes in biological pathways associated with diabetic nephropathy. PPI network analysis highlighted the central role of these genes within the relevant pathways. Predictive modeling demonstrated interactions between EMAPRDEGs, 69 miRNAs, and 117 TFs. Immune infiltration analysis showed substantial alterations in immune cell populations, with ADH1B and PC showing a significant correlation with natural killer cells and memory B cells. ROC curve analysis confirmed the diagnostic potential of EMAPRDEGs for diabetic nephropathy. qRT-PCR validated the expression patterns of CASP1, IL-18, PDK4, and FBP1, which were consistent with the bioinformatics predictions. Conclusion: Bioinformatics analysis identified 13 candidate EMAPRDEGs, among which CASP1, IL-18, PDK4, and FBP1 emerge as potential biomarkers for diabetic nephropathy.